Update app.py

app.py

@@ -1,10 +1,10 @@
 import os
-import re
-import functools
-from functools import partial
+#import re
+#import functools
+#from functools import partial
 
-import requests
-import pandas as pd
+#import requests
+#import pandas as pd
 
 import torch
 import gradio as gr
@@ -18,8 +18,6 @@ from utils import speech_to_text as stt
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 device = 0 if torch.cuda.is_available() else -1
 
-# display if the sentiment value is above these thresholds
-#thresholds = {"joy": 0.99,"anger": 0.95,"surprise": 0.95,"sadness": 0.98,"fear": 0.95,"love": 0.99,}
 color_map = {"joy": "green","anger": "red","surprise": "yellow","sadness": "blue","fear": "orange","love": "purple",}
 
 # Audio components
@@ -63,9 +61,7 @@ def sentiment(diarized, emotion_pipeline):
         if "Customer" in speaker_id:
             outputs = emotion_pipeline(sentences)
             for idx, (o, t) in enumerate(zip(outputs, sentences)):
-  #              if o["score"] > thresholds[o["label"]]:
                  customer_sentiments.append((t, o["label"]))
-
     return customer_sentiments
 
 EXAMPLES = [["Customer_Support_Call.wav"]]
@@ -95,14 +91,9 @@ with gr.Blocks() as demo:
                 cache_examples=True
             )
     # when example button is clicked, convert audio file to text and diarize
-    btn.click(
-        fn=speech_to_text,
-        inputs=audio,
-        outputs=diarized,
-    )
+    btn.click(fn=speech_to_text, inputs=audio, outputs=diarized)
     # when summarize checkboxes are changed, create summary
     sum_btn.click(fn=partial(summarize, summarization_pipeline=summarization_pipeline), inputs=[diarized], outputs=summary)
-
     # when sentiment button clicked, display highlighted text and plot
     sentiment_btn.click(fn=partial(sentiment, emotion_pipeline=emotion_pipeline), inputs=diarized, outputs=[analyzed])